Have you ever thought about what it takes to detect and prevent fraudulent activity among hundreds of millions of eCommerce transactions across the globe? What would you do to increase trust in an online marketplace where millions of buyers and sellers transact? How would you build systems that evolve over time to proactively identify and neutralize new and emerging fraud threats?
Our mission in Transaction Risk Management Services (TRMS) is to make Amazon.com the safest place to transact online. TRMS safeguards every financial transaction across all Amazon sites, while striving to ensure that these efforts are transparent to our legitimate customers. As such, TRMS designs and builds the software systems, risk models and operational processes that minimize risk and maximize trust in Amazon.com.
As a Business Intelligence Engineer in TRMS, you will be responsible for analyzing terabytes of data to identify specific instances of risk, broader risk trends and points of customer friction, developing scalable solutions for prevention. You will work with team members to ensure that the volume being flagged for manual review aligns with available capacity and Service Level Agreements (SLA’s) are met. In addition you will be responsible for building a robust set of operational and business metrics and will utilize metrics to determine improvement opportunities.
- Analyze terabytes of data to define and deliver on complex analytical deep dives to unlock insights and build scalable solutions through Data Science to ensure security of Amazon’s platform and transactions
- Build Machine Learning and/or statistical models that evaluate the transaction legitimacy and track impact over time
- Ensure data quality throughout all stages of acquisition and processing, including data sourcing/collection, ground truth generation, normalization, transformation, and cross-lingual alignment/mapping
- Define and conduct experiments to validate/reject hypotheses, and communicate insights and recommendations to Product and Tech teams
- Develop efficient data querying infrastructure for both offline and online use cases
- Collaborate with cross-functional teams from multidisciplinary science, engineering and business backgrounds to enhance current automation processes
- Learn and understand a broad range of Amazon’s data resources and know when, how, and which to use and which not to use.
- Maintain technical document and communicate results to diverse audiences with effective writing, visualizations, and presentations
- Provide mentorship and technical guidance to Data Scientists on the team